Neural Painters is a class of models that follows a GAN framework to generate brushstrokes, which are then composed to create paintings. GANs are great generative models for AI Art but they are known to be notoriously difficult to train. To overcome GAN's limitations and to speed up the Neural Painter training, we applied Transfer Learning to the process reducing it from days to only hours, while achieving the same level of visual aesthetics in the final paintings generated. We report our approach and results in this work.

I am sure you know Bob Ross and his program The Joy of Painting where he taught thousands of viewers how to paint beautiful landscapes with a simple and fun way, combining colors and brushstrokes, to achieve great results very quickly. Do you remember him teaching how to paint a pixel at the time? of course not! However, most of the current generative AI Art methods are still centered to teach machines how to'paint' at the pixel-level in order to achieve or mimic some painting style, e.g., GANs-based approaches and style transfer. This might be effective, but not very intuitive, specially when explaining this process to artists, who are familiar with colors and brushstrokes. At Libre AI, we have started a Creative AI initiative with the goal to make more accessible the advances of AI to groups of artists who do not necessarily have a tech background.

We explore neural painters, a generative model for brushstrokes learned from a real non-differentiable and non-deterministic painting program. We show that when training an agent to "paint" images using brushstrokes, using a differentiable neural painter leads to much faster convergence. We propose a method for encouraging this agent to follow human-like strokes when reconstructing digits. We also explore the use of a neural painter as a differentiable image parameterization. By directly optimizing brushstrokes to activate neurons in a pre-trained convolutional network, we can directly visualize ImageNet categories and generate "ideal" paintings of each class. Finally, we present a new concept called intrinsic style transfer. By minimizing only the content loss from neural style transfer, we allow the artistic medium, in this case, brushstrokes, to naturally dictate the resulting style.